Wellcome Open Research

Background: Sexually transmitted infections (STIs) and vaginal dysbiosis during pregnancy are associated with adverse pregnancy outcomes. Mycoplasma genitalium is the most recent STI implicated but evidence remains limited. The objectives of this study were to investigate 1) the association between M. genitalium infection during pregnancy and gestational age at delivery, preterm birth, miscarriage or stillbirth, and low birth weight and 2) the interaction with vaginal dysbiosis. Methods: We conducted a prospective cohort study in East London, South Africa. We enrolled pregnant women at gestational age <27 weeks, confirmed by ultrasound. We tested vaginal samples using nucleic acid amplification tests for M. genitalium, Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, other genital mycoplasmas and Candida spp. We defined vaginal dysbiosis using Gram-stain criteria as a Nugent score 4-10. We used quantile regression to compare the outcome in women with and without M. genitalium across the gestational age distribution, adjusting for prespecified sociodemographic and clinical characteristics and co-occurring organisms. Results: From April 1, 2021 to August 29, 2023, we enrolled 603 women, followed up 584 and obtained pregnancy outcomes for 560 (93%). Median age was 28 years (interquartile range, IQR 24, 33) and 27% of women were living with HIV. M. genitalium was detected in 44/584 (8%, 95% CI 6, 10%) and vaginal dysbiosis in 375/584 (64%) of women. Median gestational age at delivery was 39 weeks +0 days (IQR 37+4, 40+1) in women with and 39 weeks +0 days (37+4, 40+0) in those without M. genitalium. In multivariable models, associations were not observed for any adverse birth outcomes. There was no interaction between M. genitalium and vaginal dysbiosis. Discussion: M. genitalium in pregnancy was not associated with earlier gestational age at delivery or with other adverse birth outcomes. These findings do not support routine testing and treatment for M. genitalium in pregnancy.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Background Despite numerous randomized controlled trials (RCTs) and systematic reviews (SRs), current sepsis guidelines continue to issue only weak recommendations for corticosteroids. We examined the clinical scope, underlying study pools, and mortality conclusions of SRs evaluating corticosteroids for sepsis. Methods We conducted a meta-research study of SRs on corticosteroids in sepsis (2015 to 2025), extracting SR characteristics, mortality results, and included RCTs. Study-pool overlap was assessed using an SRxRCT inclusion matrix, Jaccard similarity (J), and hierarchical clustering. SRs and RCTs were classified according to standardized Population, Intervention, Comparison, Outcome (PICO) profiles. We explored discordance in short-term mortality conclusions among clinically comparable SRs and potential associations with study-pool composition, target populations, and methodological characteristics. Results Forty-two SRs including 121 unique RCTs were identified. More than half of pairwise SR comparisons shared no RCTs, and only three pairs showed high overlap (J>0.8). SRs addressing similar intervention and target population profiles frequently relied on different study pools. Among 38 SRs with short-term mortality meta-analyses, 15 (39%) reported benefit and 23 (61%) no evidence of effect. Discordance occurred exclusively among SRs evaluating broad, non-specific corticosteroid strategies; conclusions were consistent for hydrocortisone plus fludrocortisone (benefit) and hydrocortisone, ascorbic acid, and thiamine (no evidence of effect). SRs including sepsis +/- shock populations more frequently reported benefit than those restricted to septic shock (62% vs 22%), although estimates were imprecise. No single methodological or clinical factor consistently explained discordance. Conclusions SRs addressing apparently similar clinical questions frequently synthesized different underlying evidence bases and reported discordant conclusions. Guideline developers should therefore consider not only methodological quality and reported PICO, but also whether the RCTs included in an SR adequately represent the intended clinical question. Clinically coherent evidence syntheses may improve the interpretability of pooled treatment effects and support more targeted corticosteroid therapy in sepsis.

Influenza surveillance has typically been carried out using influenza-like illness (ILI) rates and proportions of laboratory tests positive for influenza as metrics to monitor, with sample sizes for the number of tests to carry out based on the precision of the resulting estimate of proportions positive. The transition out of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic period has encouraged the establishment of integrated surveillance of respiratory pathogens, in the context of multiple surveillance objectives, as set out by WHO in its revised integrated surveillance guidance and Mosaic Respiratory Surveillance Framework. These objectives include outbreak detection, situational awareness and intensity evaluation, among others. We illustrate how to design respiratory surveillance in primary care, by considering multiple surveillance objectives for different metrics of different types of respiratory pathogen circulation seasons in England, the USA and Hong Kong. We focus on a proxy of influenza activity as a metric to compare between these countries/regions. Taking advantage of England's integrated sentinel primary care surveillance system, we propose further metrics to monitor: a proxy of respiratory activity, novelly defined as the product of an acute respiratory infection (ARI) consultation rate and the proportion of tests positive for \emph{at least one pathogen}; pathogen-specific ARI-based activity proxies for more detailed monitoring of influenza and SARS-CoV-2; and integrated monitoring of proportions positive for all pathogens tested. We use a simulation approach to determine sample sizes by optimising either the probability of, or time to, detection of different events in monitored metrics, according to the different surveillance objectives. We find that sample sizes to maximise detection probabilities or minimise detection times vary by metric, objective, event and country/region. At a national level, the current sample sizes used are sufficient to detect most events in most weeks for both the USA and Hong Kong, but for England the numbers of swabs taken for ILI consultations may not be sufficient in all weeks, particularly at the start of the season when outbreak detection is important. However, broadening the criteria for swabbing to acute respiratory symptoms does allow for sufficient sample sizes.

Aims To develop a 'core' dataset of diabetes related variables to support reproducible research using UK routinely collected health data. Methods A workshop was conducted bringing together diabetes healthcare professionals, researchers, and patient and public representatives to discuss and prioritise variables for inclusion in the Diabetes Core Dataset. Core variables were those considered to be highest priority for diabetes research and available at high quality in NHS data routinely used for research (primary care [GP] and Hospital Episode Statistics [HES] data). Candidate variables for inclusion in the Diabetes Core Dataset were from a review of existing core datasets and expert opinion. Participants scored variables anonymously based on priority for diabetes research. Results 25 variables from existing diabetes core datasets and 87 other candidate variables were considered for inclusion in the Diabetes Core Dataset. All 25 of those from existing diabetes core datasets and 5 of the 87 candidate variables met the core requirements for inclusion. In addition, 7 variables were identified as high priority but not included in the core dataset as they are not currently available in GP/HES data; these were labelled as 'future high priority' variables for diabetes research. Conclusions A new diabetes core dataset for UK EHR research has been developed using a consensus-based process. The core dataset is openly available and can be flexibly applied in UK EHR (https://healthdatagateway.org/en/tool/426), including in new NHS Research Secure Data Environment platforms, to enhance reproducible research to improve the clinical care of people with diabetes and associated conditions.

Introduction Aspiration events in hospitalized adults are common and linked to substantial morbidity, mortality, and healthcare burden. While antiemetics have been studied for aspiration prevention in specific settings, evidence for their broader preventive use in hospitalized adults is limited. We conducted a systematic review and meta-analysis to evaluate the effect of prophylactic scheduled antiemetics on aspiration events in adult inpatients. Methods: Ovid MEDLINE, Ovid Embase, CINAHL, and Cochrane Library (via Wiley) were searched in May 2025. We included randomized controlled trials (RCTs) and observational studies assessing prophylactic scheduled antiemetic use in hospitalized adults, with aspiration events as the primary outcome. Secondary outcomes were aspiration pneumonia, hospital length of stay, increased oxygen requirement, ICU admission, and mortality. Two investigators screened and independently extracted data in duplicate using standardized data collection forms. Extracted information included study characteristics, patient demographics and clinical characteristics, interventions, and outcomes. Results: Three RCTs met inclusion criteria (n = 515 patients; 237 antiemetic, 278 placebo). Pooled analysis of two RCTs demonstrated an 88% relative risk [RR] reduction of aspiration events; (2.8% [3/106] prophylactic antiemetic vs 27.9% [29/104] placebo; RR = 0.12, 95% confidence intervals [CI]: 0.02-0.73, p <0.0001, low certainty) in the antiemetic group. Pneumonia rates were also lower in the antiemetic group although they did not reach statistical significance (16.5% vs 32.4%; RR 0.44, 95% CI 0.15-1.28, P = 0.13, low certainty). ICU admission rates were lower in the antiemetic group (2.6% [2/76]) vs. placebo (23% [17/74]); RR = 0.11, 95% CI: 0.03-0.48, p=0.003, very low certainty). Mortality was similar between groups (35.0% vs 38.1%; RR 1.02, 95% CI: 0.84-1.24, P = 0.85, low certainty). No adverse events were reported among all studies. Conclusion: Prophylactic scheduled antiemetics were associated with a significant reduction in aspiration and ICU admission. There was a non-significant but favorable trend towards reduction of pneumonia among hospitalized adults. Larger, high-quality trials are needed to clarify their role in aspiration prevention.

Nucleic acid extraction remains the principal infrastructure barrier to molecular influenza testing outside centralised laboratories, since bead-based purification is normally tied to mains-powered extractors and trained operators. We evaluated SmartLid, a centrifugation-free format in which a removable magnetic key shuttles paramagnetic beads through pre-aliquoted lysis/binding, wash, and elution buffers without pipetting or powered instrumentation, against an automated magnetic-bead extractor (Nextractor NX-48S) on 311 nasopharyngeal specimens from the 2024-2025 influenza season at a National Influenza Centre. Paired eluates were amplified under identical monoplex RT-qPCR conditions for influenza A(H1N1)pdm09, A(H3), and B/Victoria. Both methods gave 100% specificity (47/47 negatives; no false positives). Subtyping succeeded in 263/264 reference-positive specimens after SmartLid extraction versus 241/264 after automated extraction (99.62% versus 91.29%; difference 8.33 percentage points; discordant pairs 23 versus 1; McNemar P < 0.001). Across 240 complete pairs, cycle threshold (Ct) values were lower after SmartLid extraction (median paired difference -2.78 cycles; estimated location shift -2.60 cycles, 95% CI -2.82 to -2.37; P < 0.001) with rank-ordering of specimens conserved between methods (Spearman rho = 0.84). The advantage was preserved across all three subtypes and in both fresh and frozen specimens (adjusted P < 0.001). Specimens recovered only after SmartLid extraction had higher Ct values than dual-detected specimens (median 34.37 versus 28.54; P < 0.001), locating the gain near the assay detection limit. An instrument-free manual format can therefore exceed the extraction efficiency of an automated reference workflow, extending quality-assured influenza subtyping beyond centralised laboratories.

Introduction: Depression is common among people receiving haemodialysis and is associated with adverse outcomes, but the biological pathways underlying this relationship remain uncertain. We examined associations between depressive symptoms, mortality, immune-metabolic markers and stress-related transcriptional profiles, including potential sex differences in routine biomarkers. Methods: We studied 297 adults receiving maintenance haemodialysis across North and East London and Essex. Moderate/severe depressive symptoms were defined as a 17-item Hamilton Depression Rating Scale score >18. Cox regression examined all-cause mortality from enrolment until death or administrative censoring on 24 March 2026. Linear regression assessed associations between depressive-symptom severity and routinely measured immune-metabolic markers, overall and by sex. In a laboratory subset, inflammatory proteins and transcriptional profiles were examined overall only. Results: Moderate/severe depressive symptoms were associated with higher mortality in the adjusted model, although the association was attenuated after full adjustment (HR= 1.49; 95% CI 1.00-2.23; p=0.055). Higher white-cell count was associated with greater depressive-symptom severity before, but not after, adjustment for body mass index. Diastolic blood pressure was the only routine marker showing evidence that its association with depressive symptoms differed by sex (interaction p=0.022). Exploratory analyses did not provide convincing evidence that the measured biomarkers accounted for the depression-mortality association. In the laboratory subset, no inflammatory protein or transcriptional measure was significantly associated with depressive symptoms in fully adjusted analyses. Conclusion: Depression may identify a clinically relevant risk state among people receiving haemodialysis. The exploratory biological findings require validation in larger, prospectively sampled cohorts.

Introduction People with severe mental illness (SMI) and learning disabilities (LD) experience significant health inequalities, with diet-related conditions contributing substantially to early and preventable death. Despite high levels of nutritional risk, the presence and effectiveness of nutritional screening in mental health (MH) and LD settings remains under-researched. This study aimed to investigate nutritional screening practices in UK inpatient MH and LD services from the perspectives of dietitians. Methods A cross-sectional mixed-methods study was conducted using a novel 22-question online survey. Data was collected via the British Dietetic Association Mental Health Specialist Group (April-June 2025). Quantitative data was analysed descriptively and qualitative data by reflexive thematic analysis. Findings were integrated and presented thematically. Ethical approval was granted by Teesside University (2025Mar26544). Results Forty-seven dietitians participated, most with substantial dietetic experience, from a range of MH settings. Screening practices were widely established and supported by policy and audit. However, participants reported low confidence in screening translating into meaningful patient care. Barriers to screening included appropriateness of available tools, time constraints, difficulty engaging distressed patients and poor prioritisation of physical health. Digital integration and wider infrastructure were also important. Dietitians rarely undertook screening directly, instead holding secondary or leadership roles, while screening was most often completed by nursing staff who were often perceived to place limited importance on the process. Existing tools, particularly the Malnutrition Universal Screening Tool (MUST), were viewed as insufficiently capturing the broader nutritional risks relevant to MH/LD populations, leading some services to adopt bespoke, unvalidated tools. Conclusion Concerns regarding the suitability of existing nutritional screening tools in MH/LD settings are consistent with previous literature. However, we suggest cautious use of unvalidated bespoke tools. Whilst there was no clear front runner, MH specific tools such as the St Andrews Nutrition Screening Instrument (SANSI) and the NutriMental Screener warrant further evaluation. Importantly, findings indicate that optimising tool choice alone is unlikely to improve screening effectiveness. Nutritional screening must be embedded within clear care pathways, supported by organisational leadership, digital infrastructure, and multiprofessional engagement to move beyond procedural completion and support meaningful clinical action to improve patient care.

Abstract Objectives: Following the COVID-19 pandemic, multiple countries reported a surge in invasive group A streptococcus (iGAS) infections. Posited explanations include reduced population immunity, increased respiratory virus co-infection, and emergence of hypervirulent GAS clones. To assess the relative contribution of these factors, we analyzed the epidemiology and genomics of 3,408 iGAS infections in Scotland. Methods: National surveillance data from 2014-2024 were analyzed to characterize iGAS incidence. Hybrid whole genome sequencing was used to comprehensively genetically characterize 404 emm1 isolates collected from invasive and tonsillitis infections. Results: iGAS incidence markedly increased in late 2022 and early 2023, disproportionately affecting children and older adults. This surge was not associated with a proportional increase in bacteremia but did coincide with increased influenza and respiratory syncytial virus infections. Genomic analyses found that emm1 post-pandemic isolates were not genetically distinct from pre-pandemic isolates in genome-wide polymorphisms, accessory genes including virulence and antimicrobial resistance determinants, mobile genetic elements, or chromosomal structural variants. Conclusions: The post-pandemic iGAS surge in Scotland was not associated with emergence of a novel hypervirulent emm1 clone. Instead, the epidemiologic and population genomic findings are consistent with increased host susceptibility following reduced pathogen exposure during the pandemic and increased respiratory virus co-infection as predominant contributing factors.

Abstract Introduction: Diarrheal diseases and acute respiratory infections (ARI) disproportionately affect young children and families facing disadvantages at the individual, household, and neighborhood level. This unequal burden especially impacts children in low-and middle-income countries, such as Nepal. Data limitations impede the ability to understand children's illness episodes and treatment trajectories across the course of early childhood and their relationship to household- and neighborhood-level social determinants of health. The Chitwan Valley Family Study (CVFS) is a 30-year panel study providing a wealth of information about household- and neighborhood-level social determinants in Southern Nepal. Drawing on a cohort of young children in CVFS households, this study will measure incidents of acute illness among children under five and leverage existing data from the panel study to understand how intergenerational disadvantages, place, and other social determinants affect the frequency and duration of childhood illness and subsequent healthcare utilization. Methods and Analysis: This study will use daily symptom diaries to track children's illness symptoms (diarrhea, fever, cough, runny nose, difficulty breathing or wheezing, fatigue, loss of appetite) over the course of a year. Mother respondents will complete a baseline interview, daily symptom diaries, and a weekly phone interview with a trained interviewer to describe the prior week's symptoms and, in the case of any symptoms, healthcare utilization, treatment, expenditures, and related information. All eligible children aged 3-59 months may participate in two waves of 52 weeks of data collection. We will measure the frequency and duration of diarrhea and ARI, healthcare utilization outcomes, socio-economic status, and distance to healthcare facilities, then merge these measures with prior CVFS data related to parents' childhood circumstances, health facility characteristics, and neighborhood characteristics. Ethics and Dissemination: We received IRB approval from the Nepal Health Research Council and the University of Michigan. Informed consent will be obtained from respondents for all aspects of data collection. Identifying information will be restricted to the data collection team in Nepal and stored separately from survey data. Interviewers will check that all children with danger signs identified according to WHO/UNICEF Integrated Management of Childhood Illness clinical guidelines have received adequate treatment; a study nurse will follow up and refer those who have not. We will disseminate study findings to respondents, local partners, and nationally in Nepal, as well as in academic journals and at conferences. Datasets will be available for public and restricted-use through the Data Sharing for Demographic Research program at the Inter-university Consortium for Political and Social Research at the University of Michigan.

Public genomic repositories can support antimicrobial resistance (AMR) surveillance, but unequal sampling can bias interpretation. We characterised AMR determinants, multicountry genomic cluster overlap and surveillance gaps across Africa using an NCBI Pathogen Detection snapshot retrieved on 24 August 2026 for 55 African Union member states. Records were validated and deduplicated by BioSample, and complete AMRFinderPlus calls were summarised across five United Nations M49 subregions and eight overlapping regional economic communities (RECs). Country-pair cluster overlap was assessed using the Jaccard index, while project-based and composition-standardised sensitivity analyses evaluated repository bias. The dataset contained 86,829 unique BioSamples from 51 states; South Africa, Malawi and Kenya contributed 55.8%. Complete extended-spectrum {beta}-lactamase calls were detected in 21,513 isolates and carbapenemase calls in 4,642. blaCTX-M-15 dominated the ESBL profile, while NDM and OXA types predominated. Seventy clusters contained carbapenemase-positive isolates from at least two countries. A shared REC covered all participating countries in 38 clusters, while 32 crossed REC boundaries. Normalised country-pair overlap was low, with a maximum Jaccard index of 9.5%. Project balancing reduced the Northern African carbapenemase estimate from 32.3% to 17.9% and the Eastern African ESBL estimate from 36.9% to 12.5%. Public repositories identify determinants and clusters for investigation but do not estimate prevalence or transmission. AMR surveillance should combine national confirmation, regional institution-led investigation where countries share an REC, and continent-wide coordination through Africa CDC for cross-REC signals, supported by representative One Health sampling, standardised metadata and sustained African sequencing capacity.

Background IgA vasculitis (IgAV) is the most common systemic vasculitis in children, and its prognosis is largely determined by renal involvement (IgAV nephritis). No routine blood test identifies IgAV or stratifies the risk of nephritis, although aberrant O-glycosylation of the IgA1 hinge region is central to its pathogenesis. We developed a mass-spectrometry assay to profile IgA1 hinge O-glycoforms and define signatures of disease activity and renal involvement. Methods IgA was affinity-purified from 5 uL of plasma from 91 children (27 with acute IgAV, 26 in remission, and 38 age-matched healthy controls; 24 with and 29 without nephritis), trypsin-digested, and hinge-region O-glycopeptides were quantified by LC-MS. Sixty-nine glycoforms were normalized to a total-IgA1 tryptic peptide. Duplicate measurements showed good analytical repeatability, with a median coefficient of variation of 6%. Groups were compared using Mann-Whitney and Kruskal-Wallis tests (Benjamini-Hochberg FDR). Discrimination was assessed by ROC analysis and cross-validated logistic regression panels. Results Acute IgAV showed broad remodeling of the hinge glycoform profile (35 glycoforms differed with excellent discrimination (AUC 0.93-0.95) for the best ones), with an increase in low-sialylated, agalactosylated species and a decrease in complex sialylated species. The profile was normalized in remission (no glycoform differed from the controls). Two distinct renal patterns emerged: disease-associated glycoforms already altered without nephritis and renal-specific glycoforms altered only in nephritis (H2N2S1, H3N3S5, H3N4S4, and H4N4S3). A four-marker panel discriminated nephritis among IgAV children with a cross-validated AUC of 0.86 (IC95 % 0.75-0.94). Conclusions A single mass-spectrometry assay, from a small blood volume, captures an IgAV-associated IgA1 hinge O-glycoform signature that normalizes in remission, together with a distinct renal involvement associated signature. These findings identify candidate IgA1 O-glycoform signatures associated with IgAV activity and documented renal involvement. Prospective longitudinal studies are required to determine whether the renal-associated panel can predict subsequent nephritis.

Early detection of mammalian adaptation in highly pathogenic avian influenza A(H5) depends on genomic surveillance, yet its distribution across high-burden regions is poorly characterized. We quantified open-access (GenBank/INSDC) H5 genomic coverage relative to reported outbreak burden across nine South Asian countries during 2015-2025, linking isolates to FAO EMPRES-i/WOAH events. Of 919 H5 isolates, 814 (89%) came from one country (Bangladesh); the other eight contributed 105. India, with the largest burden (322 events), yielded only 42 isolates (13 per 100); Nepal, 2 of 78; Afghanistan, none of 5. Concentration was extreme (Gini 0.83) and unchanged by adding restricted GISAID records (1,297 combined isolates; Bangladesh 89%) or by normalizing to poultry or human population. Because reported outbreaks track reporting effort, these coverage ratios are directional, not rates. This single-country dependency, deepest where burden is highest, is a regional early-warning vulnerability.

Timely processing of routine health facility surveillance data is essential for responsive malaria control, yet the path from raw electronic reports to actionable intelligence remains a largely undocumented challenge in endemic countries. We present an open-source Extract-Transform-Load (ETL) software system and accompanying metadata R package (ramptools) that together convert raw DHIS2 health facility data into cleaned, version-controlled, analysis-ready datasets for Uganda's National Malaria Elimination Division (NMED). The ETL pipeline is implemented in R and deployed on a cloud platform with scripts running on automated schedules to keep the database current. The system extracts malaria indicators from two successive DHIS2 instances via the DHIS2 Web API, applies a two-stage outlier detection algorithm combining variance-based screening with STL decomposition, imputes missing values through seasonal interpolation, enforces logical consistency constraints across indicator cascades, and aggregates facility-level data through a six-level administrative hierarchy. All raw data are stored in an append-only versioned schema, enabling reconstruction of the database state at any historical point. The ramptools package provides standardized metadata - including an indicator crosswalk mapping 118 indicators across DHIS2 instances, a location hierarchy of 11,229 organizational units, geolocated health facility attributes, and administrative boundary shapefiles - as lazy-loaded R data objects. We validate the system through a proof-of-principle outbreak detection application that consumes ETL outputs to compute district-level outbreak indices via kernel-smoothed time series analysis, deployed as an interactive Shiny dashboard. The software has been in development since 2020, processing weekly and monthly data for approximately 8,700 health facilities. All code is open source (MIT license) and hosted on GitHub.

Background Early recognition of presentations consistent with the deliberate release of a Category A bioterrorism agent is essential for rapid isolation, public health notification, and containment. The ability of UK clinicians-in-training to recognise these syndromes is unstudied. This pilot assessed final-year UK medical students' ability to recognise these syndromes. Methods A pilot cross-sectional online survey of final-year UK medical students used single-best-answer clinical vignettes depicting syndromes associated with Category A bioterrorism agents (BT vignettes) and clinically overlapping non-bioterrorism syndromes (NBT vignettes). Performance was summarised as the proportion of vignettes correctly identified, with primary analysis comparing within-participant BT and NBT performance. Results Twenty-five participants completed the survey. Participants performed worse on BT vignettes (M = 0.55) than on NBT vignettes (M = 0.81), with a within-participant difference of -0.26 (95% CI [-0.35, -0.18]; t(24) = -6.33, p < 0.001; Cohen's dz = -1.27). Botulism (96.0%) and Ebola virus disease (88.0%) were recognised by most participants, while anthrax (40.0%), pneumonic plague (28.0%), and smallpox (24.0%) were recognised by fewer than half. Conclusion This pilot provides the first UK evidence of a substantial diagnostic deficit in final-year medical students' recognition of Category A bioterrorism agent syndromes.

Background Ventilator-associated pneumonia (VAP) is the most frequent nosocomial infection in critical care, affecting 20-36% of mechanically ventilated patients. Early prediction is hampered by the absence of a reliable, objective diagnostic standard. We developed ADVISE (Automated Dudley Ventilation Infection Series Evaluation), a machine learning model to predict physiological deterioration consistent with developing VAP using routinely collected electronic health record data from a UK NHS intensive care unit. Methods Retrospective observational study of admissions at Russell's Hall Hospital ICU (2008-2026). Following National Data Opt-Out exclusion (158 admissions, 4.2%), 3,566 admissions generated 33,208 candidate 48-hour observation blocks. Six temporal variables - FiO2, ventilator mode, P:F ratio, procalcitonin (PCT), secretion amount, and secretion description - were extracted across the baseline window (hours 1-24). A composite VAP-surrogate outcome required concurrent P:F ratio decline (>=5%) and PCT rise (>=0.5 ng/mL) across the outcome window (hours 25-48). After sequential quality filters, 2,134 blocks (18 positive, 0.84% prevalence) were retained. An XGBoost classifier was trained using nested 5-fold cross-validation with scale_pos_weight=114.0 and ROC-based hyperparameter optimisation on 1,495 training blocks, evaluated on 639 held-out test blocks. Performance was assessed via AUROC, AUPRC, and calibration (Brier score). Bootstrap resampling (1,000 iterations) generated 95% confidence intervals. Results On the held-out test set (n=639, 5 positive outcomes), ADVISE achieved AUROC 0.874 [95% CI: 0.771-0.939] and AUPRC 0.031 [0.008-0.069], representing a 4.0-fold improvement over the no-skill baseline. Nested cross-validation mean AUROC was 0.844 +/- 0.078 (range 0.716-0.915). At the Youden-optimal threshold, sensitivity was 0% with specificity 97.8%, reflecting extreme class imbalance (0.78% test prevalence). A threshold targeting 80% sensitivity achieved sensitivity 80.0% [33.3-100.0%], specificity 87.4% [84.8-89.9%], positive predictive value 4.8% [1.1-9.9%], and negative predictive value 99.8% [99.4-100.0%], detecting 4 of 5 VAP cases with approximately 80 false alarms (12.6% false positive rate). Brier score was 0.0078. Feature importance identified baseline P:F ratio as the dominant predictor (41.3% total gain), followed by ventilator mode (26.1%), secretion amount (13.2%), secretion description (9.1%), procalcitonin (5.9%), and FiO2; (4.5%). Conclusions ADVISE demonstrates that baseline oxygenation trajectory and ventilatory support patterns - derived exclusively from routinely charted ICCA variables - can identify admissions at risk of VAP-related physiological deterioration with meaningful discrimination (AUROC 0.874) despite severe class imbalance. The 80% sensitivity operating point offers a clinically actionable alert rate (12.6% FPR), supporting integration into existing ICU workflows. This proof-of-concept study establishes feasibility; multi-site prospective validation is required before clinical deployment.

Intracranial aneurysms (IA) and their rupture (subarachnoid haemorrhage, SAH) are a rare but serious complication of autosomal dominant polycystic kidney disease (ADPKD). Both genetic and environmental risk factors contribute to the pathogenesis of IA and SAH, but specific information on risk factors in the ADPKD population are limited and international clinical guidelines mainly recommend screening for ADPKD patients with a family history of IA or SAH. We assessed the associations of monogenic variants, polygenic risk, and clinical factors with the diagnosis of IA or SAH in 2,200 adult ADPKD patients from three cohorts: the Irish Kidney Gene Project (IKGP, n=475), FinnGen (n=826), and Genomics England (GEL, n=899). Polygenic risk scores (PRS) for IA, hypertension, and smoking intensity were derived from previously published GWAS summary statistics and additionally conditioned using mtCOJO to account for their genetic correlation. IA or SAH was diagnosed in 158 of 2,200 patients (7.2%; mean age at diagnosis 51.1 years). Female sex (HR 2.21, 95% CI 1.50-3.25, p=6.52x10^-5) and smoking (HR 2.06, 95% CI 1.43-2.96, p=9.49x10^-5) were associated with IA or SAH in univariate Cox proportional hazards models, while diabetes was protective (HR 0.39, 95% CI 0.22-0.70, p=1.37x10^-3). Monogenic variant status was not found to associate with increased risk of IA or SAH. Polygenic score for IA was associated with increased risk of IA or SAH, with a 1 standard deviation increase in PRS associated with a pooled hazard ratio (HR) of 1.30 (95% CI 1.09-1.57, p=4.66x10^-3), and the association of the conditioned IA PRS remained in multivariate Cox proportional hazards models accounting for clinical factors and monogenic variants (HR 1.26, 95% CI 1.03-1.55, p=0.02). The addition of polygenic scores added significant prognostic information for IA or SAH beyond clinical risk factors in FinnGen (p=3.61x10^-3) and GEL (p=8.84x10^-3) but not in IKGP, as assessed by the likelihood ratio test. Overall, our study shows that the strongest risk factors for IA or SAH in ADPKD patients are smoking history, female sex, and polygenic risk for IA. Hypertension was not associated with IA (HR 0.50, 95% CI 0.24-1.00, p=0.051), likely due to the high prevalence of hypertension in this population across cohorts (69.6-85.3%). While family history may be considered a proxy of genetic risk, this study suggests that family history itself may be of limited utility in discriminating individuals with ADPKD at risk of IA or SAH. Keywords: Intracranial Aneurysms, subarachnoid haemorrhage, Autosomal Dominant Polycystic Kidney Disease, Polygenic Risk Scores, Hypertension, Family History

Background: We sought to determine the changing prevalence, incidence, and temporal trends in real-world, recorded diagnoses of metabolic dysfunction-associated steatotic liver disease (MASLD) and assess availability of fibrosis risk stratification following awareness campaigns and guideline updates over the last decade. Methods: This population-based cohort study identified MASLD diagnoses made between 2003 to 2022 in the UK primary-care Clinical Practice Research Datalink (CPRD) to estimate prevalence and incidence. A nested case-control analysis, utilising 1:4 age-, sex-, and general practice-matched controls, assessed clinical characteristics, availability of Fibrosis-4 (Fib-4) components, and its temporal trend pre- and post-2015. Findings: 11.7 million individuals were active in CPRD in 2022. 365,797 comprised the study cohort of people with a MASLD diagnosis (matched to 1,460,288 controls). From 2012-2022, recorded MASLD prevalence rose from 0.52% [N=51,028] to 2.42% [N=283,762] (p<0.001); recorded incidence doubled from 1.60 to 3.31 per 1000 person-years (p<0.001). People with MASLD diagnosis had a higher prevalence of type 2 diabetes (21.0% [N=76,640] vs 7.7% [N=112,812]) and hypertension (35.3% [N=129,156] vs 18.7% [N=273,502]). People of South Asian ethnicity were overrepresented in MASLD cohort but had the lowest availability of Fib-4 components (14.6% [N=4,467]; adjusted odds ratio 0.67, 95% CI: 0.65-0.70, vs White). Overall, Fib-4 availability increased pre- to post-2015 (4.3% [N=4,945] to 22.8% [N=56,634]). Among those with a calculable score, fewer South Asian individuals had indeterminate/high risk (18.6% [N=833] vs 35.3% [N=15,115] in White individuals, p<0.001). Interpretation: Recorded MASLD prevalence has increased 5-fold in a decade, yet a diagnostic gap persists. Fibrosis risk stratification has improved, but remains low and is potentially inequitable for people of South Asian ethnicity. Funding: Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA; Barts Charity. Keywords: Epidemiology, Real-world data, Real-world evidence, MASLD, Primary Care

Background Severe acute respiratory infections (SARI) strain healthcare systems. Sentinel surveillance remains central to SARI monitoring, but routinely collected hospital discharge data offer a scalable, population-wide complement. In Finland, national registers now enable register-based surveillance, yet SARI case definitions remain unevaluated. Aim To evaluate whether routinely collected electronic health records can support register-based SARI surveillance and establish a national case definition. Methods We conducted a retrospective register-based study linking inpatient discharge data from the Finnish Care Register for Health Care (Hilmo) and laboratory-confirmed pathogen notifications from the National Infectious Diseases Register (NIDR). Admissions were aggregated into hospitalisation episodes using generic and pathogen-specific respiratory ICD-10 codes and linked to laboratory-confirmed respiratory pathogens within an admission-centred window. We assessed the impact of diagnostic coding position, laboratory linkage windows and alternative case definitions on age distribution, seasonality and epidemic trend detection. Results We included 145,435 respiratory hospitalisation episodes. Laboratory confirmations clustered around admission, and a -7-to-+3-day window was selected; 51,498 (35.4%) had a linked laboratory confirmation. Specific primary-position diagnoses preserved clear seasonality and age distributions consistent with SARI epidemiology, whereas secondary-position diagnoses showed attenuated seasonality. A combined case definition incorporating specific primary diagnoses and laboratory-supported syndromic episodes produced stable epidemic curves while improving sensitivity over laboratory confirmation alone. Conclusion National discharge and laboratory registers can support robust SARI surveillance in Finland when case definitions are carefully designed. A combined register-based definition balances specificity, sensitivity and feasibility, complementing sentinel surveillance and integrated respiratory monitoring. Keywords Severe acute respiratory infection (SARI); surveillance; electronic health records; ICD-10; case definition; Finland